Name
Abstract | ||
|---|---|---|
The least-squares support vector machine ( LS-SVM) is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the LS-SVM classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0129183117500152 | INTERNATIONAL JOURNAL OF MODERN PHYSICS C |
Keywords | Field | DocType |
Kernel methods, multiclass classification, big data sets | Structured support vector machine,Pattern recognition,Radial basis function kernel,Least squares support vector machine,Kernel embedding of distributions,Support vector machine,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 2 | 0129-1831 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mircea Andrecut | 1 | 73 | 8.52 |